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All Journal JURNAL ILMU SOSIAL Dia: Jurnal Administrasi Publik Publik (Jurnal Ilmu Administrasi) Journal Publicuho Jurnal Socius: Journal of Sociology Research and Education PRAJA: Jurnal Ilmiah Pemerintahan Jurnal Ilmu Komunikasi jurnal syntax admiration Nusantara Science and Technology Proceedings Public Administration Journal of Research Journal Publicuho Journal of Embedded Systems, Security and Intelligent Systems Jurnal Dinamika Hukum
Dandi Darmadi
Universitas Andi Djemma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences

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Journal : journal of embedded systems security and intelligent systems

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Simulation-Based Hybrid LSTM–XGBoost Framework for Early Prediction of Simulated Learning-Loss Risk Using LMS Log Features Agunawan; Ruslan; Dandi Darmadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13173

Abstract

Purpose – Learning loss remains a concern in Indonesian higher education after the pandemic, while LMS-based early warning systems remain limited for timely intervention. This study designs and evaluates a hybrid LSTM–XGBoost framework for early prediction of simulated LMS-based learning-loss risk, prioritizing architectural innovation over metric superiority. Methods – Using 6,440 synthetic LMS records structured from publicly documented Sevima EdLink log fields, early risk was predicted from weeks 1–4 features and labeled at week 6 with behavioral proxy rules. An 80:20 group-aware train–test split by student identifier used leakage-safe, training-only scaling and one-hot encoding. Models were compared as an untuned architectural benchmark with matched-feature ablations, approximate confidence intervals, calibration/threshold analysis, and feature importance inspection. Findings – The temporal-only LSTM model produced the strongest overall predictive performance, while the LSTM with static-feature fusion showed comparable results. The hybrid LSTM–XGBoost decision stage remained competitive but did not outperform the matched LSTM configurations or demonstrate a clear advantage over simpler baseline models. Ablation analysis further showed that neither static-feature integration nor the use of XGBoost as the final decision engine provided a meaningful performance improvement. Changes in engagement, feedback activity, interaction patterns, and early time-on-task emerged as the most influential simulated indicators of learning-loss risk. Research Implications – The hybrid architecture offers a replicable blueprint for LMS early-warning pipelines that separate temporal extraction (LSTM) from risk classification (XGBoost). Institutional use requires real LMS validation, recall optimization, and ethical compliance. Originality – This simulation-based late-fusion LSTM–XGBoost blueprint separates the prediction window (weeks 1–4) from the outcome window (week 6) and evaluates architectural contribution.
Co-Authors Abdul Rahman Nur Afrian Dwi Candranira Agunawan, Agunawan Andi Besse Panangareng Anggie Parawitha Lucca Aqshal Bagoes Avanzar Noegraha Ardian Bagus Setiawan Ardian Bagus Setiawan Aulia Regita Dewa Fitriana Aulia Regita Dewi Fitriana Bariq Rakhmadian Binti Azizatun Nafi’ah Diana Hertati Dimas Adhi Pramana Yusuf Fitriana, Aulia Regita Dewa Fitriani Ismail Gita Rania Gita Rania Haedar Djidar Hamsina Kasmad Kamal Muhammad Indrawan Jatmika Muhammad Rusdi Nur Indrayati Nur Indar Oktarizka Reviandani Pertiwi, Vidya Imanuari Rania, Gita Rasyid Thaha Reynaldus Widya Nathanie Ruslan Ruslan Salmi Salmi Setiawan, Ardian Bagus Sulastryani Sulastryani Sumainah Fauziah Yudi Burhan Yulio Astria Yudha Firnanda